About the Cardiovascular DiseaseKnowledge Portal (CVDKP) project

Data in the Cardiovascular Disease Knowledge Portal

Data in the CVDKP were generated by these consortia:

The Atrial Fibrillation Consortium (AFGen) seeks to identify the genetic basis of atrial fibrillation. Collaborators from more than 50 studies have contributed to ongoing projects, and a full list of AFGen investigators is available here.

The CARDIoGRAMplusC4D Consortium is a collaborative effort to combine data from multiple large scale genetic studies to identify risk loci for coronary artery disease and myocardial infarction.

The Knowledge Portal Framework

The Knowledge Portal framework is being developed as part of the Accelerating Medicines Partnership, a public-private partnership between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), 10 biopharmaceutical companies, and multiple non-profit organizations that is managed through the Foundation for the NIH (FNIH). AMP seeks to harness collective capabilities, scale, and resources toward improving current efforts to develop new therapies for complex, heterogeneous diseases. The ultimate goal is to increase the number of new diagnostics and therapies for patients while reducing the time and cost of developing them, by jointly identifying and validating promising biological targets for several diseases, including type 2 diabetes.

Knowledge Portals are intended to serve three key functions:

To be central repositories for large datasets of human genetic information linked to complex diseases and related traits.

To function as scientific discovery engines that can be harnessed by the community at large, and assist in the selection of new targets for drug design.

Eventually, to facilitate the conduct of customized analyses by any interested user around the world, doing so in a secure manner that provides high quality results while protecting the integrity of the data.

Knowledge Portals are intended to be secure, compliant with pertinent ethical regulations, accessible to a wide user base, inviting to researchers who may want to contribute data and participate in analyses, organic in the continuous incorporation of scientific advances, modular in their analytical capabilities and user interfaces, automated, rigorous in the quality of data aggregation and returned results, versatile, and sustainable.